Professor Eric T. Bradlow is the K.P. Chao Professor, Professor of Marketing, Statistics, Education and Economics, Chairperson of Wharton’s Marketing Department, and Vice-Dean of Analytics at Wharton. An applied statistician, Professor Bradlow uses high-powered statistical models to solve problems on everything from Internet search engines to product assortment issues. Specifically, his research interests include Bayesian modeling, statistical computing, and developing new methodology for unique data structures with application to business problems.
Eric is a fellow of the INFORMS Society for Marketing Science, a fellow of the American Statistical Association, a fellow of the American Educational Research Association, is past chair of the American Statistical Association Section on Statistics in Marketing, past Editor-in-Chief of Marketing Science, is a past statistical fellow of Bell Labs, and worked at DuPont Corporation’s Corporate Marketing and Business Research Division and the Educational Testing Service.
A prolific scholar, Professor Bradlow’s research has been published in top-tier academic journals such as the Journal of the American Statistical Association, Psychometrika, Statistica Sinica, Chance, Marketing Science, Management Science, and Journal of Marketing Research. He also serves as Associate Editor for the Journal of the American Statistical Association and the Journal of Marketing Research, and is on the Editorial Boards of Marketing Letters, Marketing Science, Journal of Marketing Research, Quantitative Marketing and Economics, and the Quarterly Journal of Electronic Commerce.
Professor Bradlow has won numerous teaching awards at Wharton, including the Linback Award for Distinguished PhD Teaching and Mentoring, the Anvil Award for MBA Education, MBA Core Curriculum teaching award, the Miller-Sherrerd MBA Core Teaching award and the Excellence in Teaching Award. His teaching interests include courses in Statistics, Marketing Research, Marketing Management and PhD Data Analysis, as well as any material related to customer analytics.
Professor Bradlow earned his PhD and Master’s degrees in Mathematical Statistics from Harvard University and his BS in Economics from the University of Pennsylvania.
Description: Bradlow, E.T., Iyengar, R., Kahn, B.E. et al. Wharton Marketing: Where Academia Meets Practice, Customer Needs and Solutions (2021)
Abstract: ‘‘More is better’’ has been a belief held by many retailers when they manage product assortments. We challenge this conventional wisdom by demonstrating that a retailer may face more price sensitive demand for existing products when expanding its assortments. To measure the effects of assortment expansion on price sensitivity, we exploit the state of Washington’s privatization of liquor sales in 2012 that generated exogenous variation in retailers’ assortments over time. We find that customers are on average more price sensitive when purchasing from other drink categories after a store started to carry liquor but its impact is heterogeneous. To understand the differential changes in the price sensitivity across product categories depending on whether they are complements or substitutes to the new one, we build a demand model that simultaneously estimates the degree of complementarity between product categories and the changes in price sensitivity upon assortment expansion. We find that the increase in the price sensitivity happens in product categories that are complements to the new one, and that these changes cannot be rationalized by alternative explanations, e.g., correlated preferences across product categories and changes in error variance. Based on the demand estimates, we conduct counterfactual simulations and show that the observed prices are consistent with retailers’ (biased) belief that the price sensitivity does not vary with assortment, which results in significant profit loss.
ludovic stourm, Raghuram Iyengar, Eric Bradlow (2020), A Flexible Demand Model for Complements Using Household Production Theory, Marketing Science, 39, pp. 763-787.
Abstract: Advances in data collection have made it increasingly easy to collect information on advertising exposures. However, translating this seemingly rich data into measures of advertising response has proven difficult, largely because of concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual holdout experiments shows positive effects for both email and catalog; however, the estimated effect for any individual campaign is imprecise, because of the small size of the holdout. To pool data across campaigns, we develop a hierarchical Bayesian model for advertising response that allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and we find that targeting the most responsive customers increases the predicted returns on advertising by approximately 70% versus traditional recency, frequency, and monetary value–based targeting.
Julie Novak, Eleanor McDonnell Feit, Shane T. Jensen, Eric Bradlow (Working), Bayesian Imputation for Anonymous Visits.
Abstract: Customers often stockpile reward points in linear loyalty programs (i.e., programs that do not explicitly reward stockpiling) despite several economic incentives against it (e.g., the time value of money). The authors develop a mathematical model of redemption choice that unites three explanations for why customers seem to be motivated to stockpile on their own, even though the retailer does not reward them for doing so. These motivations are economic (the value of forgone points), cognitive (nonmonetary transaction costs), and psychological (customers value points differently than cash). The authors capture the psychological motivation by allowing customers to book cash and point transactions in separate mental accounts. They estimate the model on data from an international retailer using Markov chain Monte Carlo methods and accurately forecast redemptions during an 11-month out-of-sample period. The results indicate substantial heterogeneity in how customers are motivated to redeem and suggest that the behavior in the data is driven mostly by cognitive and psychological incentives.
Yao Zhang, Eric Bradlow, Dylan Small (2015), Predicting Customer Value Using Clumpiness: From RFM to RFMC, Marketing Science, 34 (2), pp. 195-208.
This course addresses how to design and implement the best combination of marketing efforts to carry out a firm's strategy in its target markets. Specifically, this course seeks to develop the student's (1) understanding of how the firm can benefit by creating and delivering value to its customers, and stakeholders, and (2) skills in applying the analytical concepts and tools of marketing to such decisions as segmentation and targeting, branding, pricing, distribution, and promotion. The course uses lectures and case discussions, case write-ups, student presentations, and a comprehensive final examination to achieve these objectives.
Building upon Marketing 611, the goal of this course is to develop skills in formulating and implementing marketing strategies for brands and businesses. The course will focus on issues such as the selection of which businesses and segments to compete in, how to allocate resources across businesses, segments, and elements of the marketing mix, as well as other significant strategic issues facing today's managers in a dynamic competitive environment. A central theme of the course is that the answer to these strategic problems varies over time depending on the stage of the product life cycle at which marketing decisions are being made. As such, the PLC serves as the central organizing vehicle of the course. We will explore such issues as how to design optimal strategies for the launch of new products and services that arise during the introductory phase, how to maximize the acceleration of revenue during the growth phase, how to sustain and extend profitability during the mature phase, and how to manage a business during the inevitable decline phase.
Building upon Marketing 611, Marketing 613 is an intensive immersion course designed to develop skills in formulating and implementing marketing strategies for brands and businesses. The central activity will be participation in a realistic integrative product management simulation named SABRE. In SABRE, students will form management teams that oversee all critical aspects of modern product management: the design and marketing of new products, advertising budgeting and design, sales force sizing and allocation, and production planning. As in the real world, teams will compete for profitability, and the success that each team has in achieving this goal will be a major driver of the class assessment. The SABRE simulation is used to convey the two foci of learning in the course: the changing nature of strategic problems and their optimal solutions as industries progress through the product life cycle, and exposure to the latest analytic tools for solving these problems. Specifically, SABRE management teams will receive training in both how to make optimal use of marketing research information to reduce uncertainty in product design and positioning, as well as decision support models to guide resource allocation.
A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.
This course is designed to generate knowledge of the use of quantitative statistical, econometric, and Machine Learning methods and their application to Marketing problems. A strong emphasis is also placed on the applied nature of applying these methods in terms of data requirements, exogenous versus endogenous variation, and computational challenges when using complex models. Students outside of Marketing are welcome, and we discuss how these models can be applied to other disciplines. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.
This course is designed to generate awareness and appreciation of the way several substantive topics in marketing have been studied empirically using quantitative models. This seminar reviews empirical models of marketing phenomena including consumer choice, adoption of new products, sales response to marketing mix elements, and competitive interaction. Applies methods and concepts developed in econometrics and statistics but focuses on substantive issues of model structure and interpretation, rather than on estimation techniques. Ultimately, the goals are a) to prepare students to read and understand the literature and b) to stimulate new research interests. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.
Requires written permission of instructor and the department graduate adviser.
Written permission of instructor and the department course coordinator required to enroll.
Wharton School, MBA Excellence in Teaching Award
Fellow of the American Statistical Association
Named the first recipient of The K.P. Chao Professorship
“A Learning-based Model for Imputing Missing Levels in Partial Conjoint Profiles,” co-authored with Y. Hue and T-H Ho, lead article and discussion paper, Vol. XLI (November 2004), 369-38
2003, 2004, 2005
AERA Outstanding Reviewer
Wharton West WEMBA Teaching Award
1999, 2000, 2001, 2002
1998, 1999, 2001
Appointed Research Consultant, AT&T Bell Laboratories
Finalist, American Statistical Association Savage Award Dissertation Prize
Corporate Marketing Division
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